Unsupervised Visuomotor Control through Distributional Planning Networks

14 Feb 2019 Tianhe Yu Gleb Shevchuk Dorsa Sadigh Chelsea Finn

While reinforcement learning (RL) has the potential to enable robots to autonomously acquire a wide range of skills, in practice, RL usually requires manual, per-task engineering of reward functions, especially in real world settings where aspects of the environment needed to compute progress are not directly accessible. To enable robots to autonomously learn skills, we instead consider the problem of reinforcement learning without access to rewards... (read more)

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